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In this article, we introduce and evaluate the concept of robosourcing forcreating educational content. Robosourcing lies in the intersection ofcrowdsourcing and large language models, where instead of a crowd of humans,requests to large language models replace some of the work traditionallyperformed by the crowd. Robosourcing includes a human-in-the-loop to providepriming (input) as well as to evaluate and potentially adjust the generatedartefacts; these evaluations could also be used to improve the large languagemodels. We propose a system to outline the robosourcing process. We furtherstudy the feasibility of robosourcing in the context of education by conductingan evaluation of robosourced and programming exercises, generated using OpenAICodex. Our results suggest that robosourcing could significantly reduce humaneffort in creating diverse educational content while maintaining qualitysimilar to human-created content.

Robosourcing Educational Resources -- Leveraging Large Language Models for Learnersourcing